Identifying Patient Groups based on Frequent Patterns of Patient Samples

Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes

Conformance analysis of clinical pathway using electronic health record data

Objectives: The objective of this study was to confirm the conformance rate of the actual usage of the clinical pathway (CP) using Electronic Health Record (EHR) log data in a tertiary general university hospital to improve the CP by reflecting realworld care processes. Methods: We analyzed the application and matching rates of clinicians??? orders with predefined CP order sets based on data from 164 inpatients who received appendectomies out of all patients who were hospitalized from August 2013 to June 2014. We collected EHR log data on patient information, medication orders, operation performed, diagnosis, transfer, and CP order sets. The data were statistically analyzed. Results: The average value of the actual application rate of the prescribed CP order ranged from 0.75 to 0.89. The application rate decreased when the order date was factored in along with the order code and type. Among CP pre-operation, intra-operation, post-operation, routine, and discharge orders, orders pertaining to operations had higher application rates than other types of orders. Routine orders and discharge orders had lower application rates. Conclusions: This analysis of the application and matching rates of CP orders suggests that it is possible to improve these rates by updating the existing CP order sets for routine discharge orders to reflect data-driven evidence. This study shows that it is possible to improve the application and matching rates of the CP using EHR log data. However, further research should be performed to analyze the effects of these rates on care outcomes. © 2015 The Korean Society of Medical Informaticsopen0

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given